About this repository

This code repository replicates the findings in "The Effectiveness of a
Neighbor-to-Neighbor Get-Out-the-Vote Program: Evidence from the
2017 Virginia State Elections."

To replicate the findings, run 00_replicate_all.R. The log file for this
script is included as 00_replicate_all.log.

Note: Materials for Figure 2 of the paper are not included in this archive
in order to protect the privacy of study participants.

Description of data files

voter_pairs.csv: Pair-level dataset to replicate the pair-level regressions

Variable descriptions
pair_id:	Integer id for pair
Volunteer_In_District:	1 if volunteer was in the same district as pair,
0 otherwise
volunteer_id:	Integer id for volunteer
BinaryTurnout_treatment:	1 if treated voter turned out to vote, 0 otherwise
Black_treatment:	1 if treated voter is black, 0 otherwise
Hispanic_treatment:	1 if treated voter is hispanic, 0 otherwise
Asian_treatment:	1 if treated voter is asian, 0 otherwise
Male_treatment:	1 if treated voter is male, 0 otherwise
Under30_treatment:	1 if treated voter is under 30, 0 otherwise
VotePropensity_treatment:	Catalist vote propensity score for treated voter
Partisanship_treatment:	Catalist democratic partisanship score for treated voter
BinaryTurnout_control:	1 if control voter turned out to vote, 0 otherwise
Black_control:	1 if control voter is black, 0 otherwise
Hispanic_control:	1 if control voter is hispanic, 0 otherwise
Asian_control:	1 if control voter is asian, 0 otherwise
Male_control:	1 if control voter is male, 0 otherwise
Under30_control:	1 if control voter is under 30, 0 otherwise
VotePropensity_control:	Catalist vote propensity score for control voter
Partisanship_control:	Catalist democratic partisanship score for control voter
average_distance_miles:	Average distance between volunteer and each voter
in pair (miles)
same_household:	1 if volunteer was in the same household as any member of
the pair, 0 otherwise
assignment_factor:	1 for first pair, 2 for second pair, 3 for third pair,
4+ for pairs four or higher


voters.csv: Voter-level dataset to replicate the voter-level regressions

Variable descriptions
voter_id:	Integer id for voter
pair_id:	Integer id for pair
volunteer_id:	Integer id for volunteer
District:	State delegate district of pair
Volunteer_In_District:	1 if volunteer was in the same district as pair,
0 otherwise
Treatment:	1 if voter assigned to treatment, 0 otherwise
assignment_factor:	1 for first pair, 2 for second pair, 3 for third pair,
4+ for pairs four or higher
same_household:	1 if volunteer was in the same household as any member of
the pair, 0 otherwise
average_distance_miles:	Average distance between volunteer and each voter
in pair (miles)
White:	1 if voter is white, 0 otherwise
Black:	1 if voter is black, 0 otherwise
Hispanic:	1 if voter is hispanic, 0 otherwise
Asian:	1 if voter is asian, 0 otherwise
Male:	1 if voter is male, 0 otherwise
Under30:	1 if voter is under 30, 0 otherwise
VotePropensity:	Catalist vote propensity score
Partisanship:	Catalist democratic partisanship score
Postcard_Binary:	1 if volunteer reported contacting voter by postcard,
0 otherwise
Social_Media_Binary:	1 if volunteer reported contacting voter by social media,
0 otherwise
Email_Binary:	1 if volunteer reported contacting voter by email, 0 otherwise
Phone_Binary:	1 if volunteer reported contacting voter by phone, 0 otherwise
In_Person_Binary:	1 if volunteer reported contacting voter in person,
0 otherwise
Text_Binary:	1 if volunteer reported contacting voter by text message,
0 otherwise
Other_Binary:	1 if volunteer reported contacting voter by other means,
0 otherwise
Method_Count:	Number of unique ways the volunteer reported contacting the voter
days_elapsed:	Number of days elapsed between voter assignment date and
first update date (defaults to length of the program if no updates submitted,
NA for control voters)
total_updates:	Total number of updates submitted by the volunteer for voter,
NA if no updates submitted. Updates include any kind of form submission,
even if no actual contact was reported.
End_Status_In_Progress:	1 if volunteer reported “in progress” as the last
status update for voter, 0 otherwise
End_Status_Success:	1 if volunteer reported “success” as the last status update
for voter, 0 otherwise
BinaryTurnout:	1 if voter turned out to vote, 0 otherwise

How the final datasets were created

The data on voter assignments and updates from the volunteers were collected
and stored in Qualtrics. These datasets were merged to a Catalist data export
containing voter turnout and demographic information using Catalist's unique
voter id. A small percentage of volunteers signed up for the program through
multiple accounts. These accounts were consolidated based on manual checks of
volunteers' full names and home addresses, so that each volunteer had only set
of assigned voters in the final data. The data were reshaped to wide format
by pair id for the pair-level dataset.
